


#chapter 5


def weight_variable(shape):
	initial = tf.truncated_normal(shape, stddev=0.1)
	return tf.Variable(initial)
	
def bias_variable(shape):
	initial = tf.constant(0.1, shape=shape)
	return tf.Variable(initial)
	
def conv2d(x, W):
	return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
	
def max_pool_2x2(x):
	return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
	
	
	

import tensorflow as tf
sess = tf.InteractiveSession()

#step 1: define network forward function

x=tf.placeholder(tf.float32, [None, 784])
x_image=tf.reshape(x, [-1,28,28,1])

W_conv1=weight_variable([5,5,1,32])
b_conv1=bias_variable([32])
h_conv1=tf.nn.relu(conv2d(x_image, W_conv1)+b_conv1)

h_pool1=max_pool_2x2(h_conv1)

W_conv2=weight_variable([5,5,32,64])
b_conv2=bias_variable([64])
h_conv2=tf.nn.relu(conv2d(h_pool1, W_conv2)+b_conv2)

h_pool2=max_pool_2x2(h_conv2)

h_pool2_flat=tf.reshape(h_pool2, [-1, 7*7*64])

W_fc1=weight_variable([7*7*64,1024])
b_fc1=bias_variable([1024])
h_fc1=tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1)+b_fc1)

keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

W_fc2 = weight_variable([1024, 10])
b_fc2 = bias_variable([10])
y = tf.nn.softmax(tf.matmul(h_fc1_drop, W_fc2) + b_fc2)


#end step 1


#step 2a define loss function

y_ = tf.placeholder(tf.float32, [None, 10])
cross_entropy=tf.reduce_mean(-tf.reduce_sum(y_*tf.log(y), reduction_indices=[1]))

#end step 2a

#step 2b define optimizer

train_step=tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

#end step 2b

#step 2c define evaluate function

correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

#end step 2c

tf.global_variables_initializer().run()

#step 3a load training set

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
#end step 3a


#step 3b loop training


for i in range(2000):
	batch_xs, batch_ys = mnist.train.next_batch(50)
	
	if i%100==0:
		train_accuracy=accuracy.eval(feed_dict={x:batch_xs, y_:batch_ys, keep_prob:1.0})
		print("step %d, training accuracy %g"%(i, train_accuracy))
	
	train_step.run({x: batch_xs, y_: batch_ys, keep_prob: 0.5})
	
#end step 3b

#step 4 evaluate on test set

print(accuracy.eval({x: mnist.test.images, y_:mnist.test.labels, keep_prob: 1.0}))

#end step 4




print(W_conv1.eval().size+b_conv1.eval().size+W_conv2.eval().size+b_conv2.eval().size+W_fc1.eval().size+b_fc1.eval().size+W_fc2.eval().size+b_fc2.eval().size)

print(W_conv1.eval().shape)

print(W_conv1.eval())


print(b_conv1.eval().shape)

print("2222222222222222222222222222222222222222222222222222222")

print(W_conv2.eval())
print(b_conv2.eval())

import numpy
#numpy.savetxt("/home/u/W_conv1.csv", W_conv1.eval(), delimiter=",")
#numpy.savetxt("/home/u/b_conv1.csv", b_conv1.eval(), delimiter=",")
#numpy.savetxt("/home/u/W_conv2.csv", W_conv2.eval(), delimiter=",")
#numpy.savetxt("/home/u/b_conv2.csv", b_conv2.eval(), delimiter=",")

print(W_fc1.eval().shape)
numpy.savetxt("/home/u/W_fc1.csv", W_fc1.eval(), delimiter=",")
print(b_fc1.eval().shape)
numpy.savetxt("/home/u/b_fc1.csv", b_fc1.eval(), delimiter=",")
print(W_fc2.eval().shape)
numpy.savetxt("/home/u/W_fc2.csv", W_fc2.eval(), delimiter=",")
print(b_fc2.eval().shape)
numpy.savetxt("/home/u/b_fc2.csv", b_fc2.eval(), delimiter=",")
